文献类型：期刊文献

英文题名：Sequential Dependence Modeling Using Bayesian Theory and D-Vine Copula and Its Application on Chemical Process Risk Prediction

作者：Ren, Xiang[1];Li, Shaojun[1];Lv, Cheng[1];Zhang, Ziyang[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2014

卷号：53

期号：38

起止页码：14788

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20151000610462);WOS:【SCI-EXPANDED(收录号:WOS:000342328400024)】；

基金：The authors appreciate the National Natural Science Foundation of China (Project 21176072) and the Fundamental Research Funds for the Central Universities for their financial support.

语种：英文

外文关键词：Higher order statistics - Intelligent systems - Markov processes - Monte Carlo methods - Backpropagation - Neural networks

摘要：An emerging kind of prediction model for sequential data with multiple time series is proposed. Because D-vine copula provides more flexibility in dependence modeling, accounting for conditional dependence, asymmetries, and tail dependence, it is employed to describe sequential dependence between variables in the sample data. A D-vine model with the form of a time window is created to fit the correlation of variables well. To describe the randomness dynamically, Bayesian theory is also applied. As an application, a detailed modeling of prediction of abnormal events in a chemical process is given. Statistics (e.g., mean, variance, skewness, kurtosis, confidence interval, etc.) of the posterior predictive distribution are obtained by Markov chain Monte Carlo simulation. It is shown that the model created in this paper achieves a prediction performance better than that of some other system identification methods, e.g, autoregressive moving average model and back propagation neural network.